Methods and systems for an electric aircraft coaching simulator

Abstract

A system and method for an electric aircraft coaching simulator is illustrated. The simulator includes a pilot control that is configured to generate a pilot command. The simulator includes a processor that is configured to receive optimized flight data, simulate a battery performance of an electric aircraft as a function of the received pilot command and generate an optimal flight recommendation based off the optimized flight data. The simulation model is configured to communicatively connect with the flight simulator to mimic a real flight situation. Optimized flight data from other pilots can be used to coach pilots how to fly with more energy efficiency.

What is claimed is: 
     
       1. A system for an electric aircraft coaching simulator, wherein the system comprises:
 a pilot control configured to generate a pilot command as function of a user interaction with the pilot control; 
 a computing device; 
 at least a processor; and 
 a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to:
 receive historical flight data; 
 simulate performance of an electric aircraft as a function of the historical flight data and the pilot command, wherein the simulated performance of the electric aircraft comprises a simulated battery performance of the simulated electric aircraft; 
 generate an optimal flight recommendation as a function of the historical flight data and the simulated battery performance, wherein generating an optimal flight recommendation comprises:
 receiving a pilot performance; 
 determining an energy efficiency datum, utilizing a machine-learning model, based on the pilot performance, wherein the energy efficiency datum refers to energy used by pilot performance in relation to an amount of energy need for flight simulation; and 
 assigning a score to the energy efficiency datum based on an energy efficiency level of the pilot; and 
 
 communicate the optimal flight recommendation to a user. 
 
 
     
     
       2. The system of  claim 1 , wherein the memory contains instructions further configuring the at least a processor to validate simulated battery performance as a function of historical battery performance data of the historical flight data. 
     
     
       3. The system of  claim 1 , wherein simulating the simulated battery performance of the simulated electric aircraft further comprises:
 simulating a flight of the simulated electric aircraft as a function of the historical flight data; 
 validating the simulated battery performance as a function of historical battery performance data of the historical flight data. 
 
     
     
       4. The system of  claim 3 , wherein simulating the simulated flight further comprises displaying a visual representation of the simulated further comprises adjacent displays. 
     
     
       5. The system of  claim 1 , further comprises a plurality of flight simulator components. 
     
     
       6. The system of  claim 1 , wherein generating an optimal flight recommendation further comprises generating an energy efficiency datum representative of the pilot's performance as a function of the pilot command. 
     
     
       7. The system of  claim 6 , wherein the energy efficiency datum is a function of an energy usage datum. 
     
     
       8. The system of  claim 1 , wherein generating an optimal flight recommendation further comprises:
 training a machine-learning model using training data comprising a plurality of inputs containing flight data correlated to a plurality of outputs containing an energy efficiency datum; 
 receiving simulated flight data as a function of the pilot's performance; 
 inputting the simulated flight data into the machine-learning model; and 
 generating the optimal flight recommendation as a function of the machine-learning model and the simulated flight data. 
 
     
     
       9. The system of  claim 1 , wherein generating an optimal flight recommendation further comprises receiving an energy efficiency datum. 
     
     
       10. The system of  claim 9 , wherein the energy efficiency datum is a function of a pilot's performance. 
     
     
       11. A method for an electric aircraft coaching simulator, the method comprising:
 generating a pilot command as a function of a user interaction, using a pilot control; 
 receiving, by at least a processor a computing device containing a memory, historical flight data; 
 simulating, by the at least a processor, a battery performance of an electric aircraft as a function of the historical flight data and the pilot command; 
 generating, by the at least a processor, an optimal flight recommendation as a function of the historical flight data and the simulated battery performance, wherein
 generating an optimal flight recommendation comprises: 
 receiving a pilot performance; 
 determining an energy efficiency datum, utilizing a machine-learning model, based on the pilot performance, wherein the energy efficiency datum refers to energy used by pilot performance in relation to an amount of energy need for flight simulation; and 
 assigning a score to the energy efficiency datum based on an energy efficiency level of the pilot; and 
 
 communicating, by the at least a processor, the optimal flight recommendation to a user. 
 
     
     
       12. The method of  claim 11 , further comprising validating, by the at least a processor, simulated battery performance as a function of historical battery performance data of the historical flight data. 
     
     
       13. The method of  claim 11 , wherein simulating the simulated battery performance of the simulated electric aircraft further comprises:
 simulating a flight of the simulated electric aircraft as a function of the historical flight data; 
 validating the simulated battery performance as a function of historical battery performance data of the historical flight data. 
 
     
     
       14. The method of  claim 13 , wherein simulating the simulated flight further comprises displaying a visual representation of the simulated further comprises adjacent displays. 
     
     
       15. The method of  claim 11 , further comprising simulating using a plurality of flight simulator components. 
     
     
       16. The method of  claim 11 , wherein generating an optimal flight recommendation further comprises generating an energy efficiency datum representative of the pilot's performance as a function of the pilot command. 
     
     
       17. The method of  claim 16 , wherein the energy efficiency datum is a function of an energy usage datum. 
     
     
       18. The method of  claim 11 , wherein generating an optimal flight recommendation further comprises:
 training a machine-learning model using training data comprising a plurality of inputs containing flight data correlated to a plurality of outputs containing an energy efficiency datum; 
 receiving simulated flight data as a function of the pilot's performance; 
 inputting the simulated flight data into the machine-learning model; and 
 generating the optimal flight recommendation as a function of the machine-learning model and the simulated flight data. 
 
     
     
       19. The method of  claim 11 , wherein generating an optimal flight recommendation further comprises receiving an energy efficiency datum. 
     
     
       20. The method of  claim 19 , wherein the energy efficiency datum is a function of a pilot's performance.